Getting Started
Setting up R packages
packages = c('tidyverse', 'sf', 'tmap', 'lubridate', 'clock', 'sftime', 'rmarkdown')
for (p in packages){
if(!require(p, character.only = T)){
install.packages(p)
}
}
Importing data
schools <- read_sf("data/Schools.csv",
options = "GEOM_POSSIBLE_NAMES=location")
print(schools)
Simple feature collection with 4 features and 4 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: -4701.463 ymin: 1607.984 xmax: -376.7505 ymax: 6556.032
CRS: NA
# A tibble: 4 x 5
schoolId monthlyCost maxEnrollment location
<chr> <chr> <chr> <POINT>
1 0 12.81244502 242 (-376.7505 1607.984)
2 450 91.14351385 418 (-2597.448 3194.155)
3 900 38.00537955 394 (-2539.158 6556.032)
4 1350 73.19785215 384 (-4701.463 5141.763)
# ... with 1 more variable: buildingId <chr>
buildings <- read_sf("data/buildings.csv",
options = "GEOM_POSSIBLE_NAMES=location")
print(buildings)
Simple feature collection with 1042 features and 4 fields
Geometry type: POLYGON
Dimension: XY
Bounding box: xmin: -4762.191 ymin: -30.08359 xmax: 2650 ymax: 7850.037
CRS: NA
# A tibble: 1,042 x 5
buildingId location buildingType maxOccupancy
<chr> <POLYGON> <chr> <chr>
1 1 ((350.0639 4595.666, 390.0633~ Commercial ""
2 2 ((-1926.973 2725.611, -1948.1~ Residental "12"
3 3 ((685.6846 1552.131, 645.9985~ Commercial ""
4 4 ((-976.7845 4542.382, -1053.2~ Commercial ""
5 5 ((1259.306 3572.727, 1299.255~ Residental "2"
6 6 ((478.8969 1082.484, 473.6596~ Commercial ""
7 7 ((-1920.823 615.7447, -1960.8~ Residental ""
8 8 ((-3302.657 5394.354, -3301.5~ Commercial ""
9 9 ((-600.5789 4429.228, -495.95~ Commercial ""
10 10 ((-68.75908 5379.924, -28.782~ Residental "5"
# ... with 1,032 more rows, and 1 more variable: units <chr>
pubs <- read_sf("data/pubs.csv",
options = "GEOM_POSSIBLE_NAMES=location")
apartments <- read_sf("data/apartments.csv",
options = "GEOM_POSSIBLE_NAMES=location")
employers <- read_sf("data/employers.csv",
options = "GEOM_POSSIBLE_NAMES=location")
restaurants <- read_sf("data/restaurants.csv",
options = "GEOM_POSSIBLE_NAMES=location")
tmap_mode("view")
tm_shape(buildings)+
tm_polygons(col = "grey60",
size = 1,
border.col = "black",
border.lwd = 1)
tmap_mode("view")
#tmap_mode("plot")
tm_shape(buildings)+
tm_polygons(col = "grey60",
size = 1,
border.col = "black",
border.lwd = 1) +
tm_shape(employers) +
tm_dots(col = "red")+
tm_shape(schools) +
tm_dots(col = "yellow")+
tm_shape(pubs) +
tm_dots(col = "blue")+
tm_shape(apartments) +
tm_dots(col = "green")+
tm_shape(restaurants) +
tm_dots(col = "purple")
logs <- read_sf("data/ParticipantStatusLogs1.csv",
options = "GEOM_POSSIBLE_NAMES=currentLocation")
logs_selected <- logs %>%
mutate(Timestamp = date_time_parse(timestamp, zone="", format = "%Y-%m-%dT%H:%M:%S")) %>%
mutate(day=get_day(Timestamp)) %>%
filter(currentMode == "Transport")
write_rds(logs_selected, "data/rds/logs_selected.rds")
logs_selected <- read_rds("data/rds/logs_selected.rds")
tmap_mode("plot")
tm_shape(buildings)+
tm_polygons(col = "grey60",
size = 1,
border.col = "black",
border.lwd = 1) +
tm_shape(logs_selected) +
tm_dots(col = "red")
hex <- st_make_grid(buildings,
cellsize=100,
square=FALSE) %>%
st_sf() %>%
rowid_to_column('hex_id')
plot(hex)
points_in_hex <- st_join(logs_selected,
hex,
join=st_within)
#plot(points_in_hex, pch='.')
points_in_hex <- st_join(logs_selected,
hex,
join=st_within) %>%
st_set_geometry(NULL) %>%
count(name='pointCount', hex_id)
head(points_in_hex)
# A tibble: 6 x 2
hex_id pointCount
<int> <int>
1 169 35
2 212 56
3 225 21
4 226 94
5 227 22
6 228 45
hex_combined <- hex %>%
left_join(points_in_hex,
by = 'hex_id') %>%
replace(is.na(.), 0)
tm_shape(hex_combined %>%
filter(pointCount > 0))+
tm_fill("pointCount",
n = 8,
style = "quantile") +
tm_borders(alpha = 0.1)
logs_path <- logs_selected %>%
group_by(participantId, day) %>%
summarize(m = mean(Timestamp),
do_union=FALSE) %>%
st_cast("LINESTRING")
logs_path_0 <- logs_path%>%filter(`participantId` == "0")
tmap_mode("plot")
tm_shape(buildings)+
tm_polygons(col = "grey60",
size = 1,
border.col = "black",
border.lwd = 1) +
tm_shape(logs_path_0) +
tm_lines(col = "blue")
tmap_mode("plot")
tm_shape(buildings)+
tm_polygons(col = "grey60",
size = 1,
border.col = "black",
border.lwd = 1) +
tm_shape(logs_path_0) +
tm_lines(col = "blue")+
tm_facets(by="day")
